Why Snowpark brings code to the data in Snowflake - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Snowpark session.

Snowflake

from snowflake.snowpark import Session

connection_parameters = {
    "account": "[1]",
    "user": "user_name",
    "password": "password",
    "role": "SYSADMIN",
    "warehouse": "COMPUTE_WH",
    "database": "MY_DB",
    "schema": "PUBLIC"
}
session = Session.builder.configs(connection_parameters).create()

Drag options to blanks, or click blank then click option'

Amy_warehouse

Bmy_account

Cmy_database

Dmy_schema

Attempts:

3 left

💡 Hint

Common Mistakes

Using warehouse or database names instead of the account identifier.

Leaving the account parameter empty.

The account parameter specifies your Snowflake account identifier, which is required to establish a session.

2fill in blank

medium

Complete the code to create a DataFrame from a Snowflake table.

Snowflake

df = session.table("[1]")

Drag options to blanks, or click blank then click option'

Amy_table

Bmy_warehouse

Cmy_database

Dmy_schema

Attempts:

3 left

💡 Hint

Common Mistakes

Using warehouse or database names instead of the table name.

Omitting quotes around the table name.

The table method requires the table name to create a DataFrame representing that table.

3fill in blank

hard

Fix the error in the code to apply a filter on the DataFrame.

Snowflake

filtered_df = df.filter(df["AGE"] [1] 30)

Drag options to blanks, or click blank then click option'

A==

B=>

C=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '==' or '>'.

Using '=>' which is invalid syntax.

The correct operator to filter rows where AGE is greater than 30 is >.

4fill in blank

hard

Fill both blanks to select columns and show the first 5 rows.

Snowflake

result = df.select([1]).limit([2]).collect()

Drag options to blanks, or click blank then click option'

A"NAME", "AGE"

B5

C10

D"SALARY"

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting only one column when multiple are needed.

Using limit(10) instead of limit(5).

Selecting columns "NAME" and "AGE" and limiting to 5 rows shows a small sample of the data.

5fill in blank

hard

Fill all three blanks to create a new column with a calculation and filter rows.

Snowflake

from snowflake.snowpark.functions import col

new_df = df.withColumn("AGE_PLUS_SALARY", col("AGE") [1] col("SALARY")).filter(col("AGE_PLUS_SALARY") [2] [3])

Drag options to blanks, or click blank then click option'

A+

B>

C1000

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' instead of '+' for addition.

Using '<' instead of '>' for filtering.

Using a wrong threshold value.

The code adds AGE and SALARY columns, then filters rows where the sum is greater than 1000.

Practice

1. What is the main benefit of Snowpark bringing code to the data inside Snowflake?

easy

A. It increases data duplication across systems.

B. It requires exporting data to external servers.

C. It only supports SQL queries.

D. It reduces data movement and improves performance.

Solution

1. Step 1: Understand Snowpark's design

   Snowpark runs code inside Snowflake, close to the data storage.

2. Step 2: Identify benefits of running code near data

   This reduces the need to move large data sets, saving time and resources.

3. Final Answer:

   It reduces data movement and improves performance. -> Option D

4. Quick Check:

   Code near data = less data movement [OK]

Hint: Code runs inside Snowflake, so data stays put [OK]

Common Mistakes:

• Thinking data must be exported to run code
• Believing Snowpark only supports SQL
• Assuming data is duplicated across systems

2. Which of the following is the correct way to create a Snowpark session in Python?

easy

A. session = SnowflakeSession.create(connection_parameters)

B. session = snowpark.connect(connection_parameters)

C. session = snowflake.snowpark.Session.builder.configs(connection_parameters).create()

D. session = snowflake.connect_snowpark(connection_parameters)

Solution

1. Step 1: Recall Snowpark Python session creation syntax

   Snowpark uses Session.builder.configs(...).create() to start a session.

2. Step 2: Match the correct method call

   session = snowflake.snowpark.Session.builder.configs(connection_parameters).create() matches the official syntax for creating a session in Snowpark Python API.

3. Final Answer:

   session = snowflake.snowpark.Session.builder.configs(connection_parameters).create() -> Option C

4. Quick Check:

   Session.builder.configs(...).create() = correct syntax [OK]

Hint: Look for builder.configs(...).create() pattern [OK]

Common Mistakes:

• Using incorrect method names like connect or connect_snowpark
• Missing builder or configs in the call
• Confusing Snowpark session with generic Snowflake connection

3. Given this Snowpark Python code snippet:

df = session.table('EMPLOYEES')
filtered = df.filter(df['DEPARTMENT'] == 'SALES')
count = filtered.count()

What does count represent?

medium

A. The number of rows where DEPARTMENT is 'SALES'.

B. The number of columns in the EMPLOYEES table.

C. The total number of rows in the EMPLOYEES table.

D. The number of distinct departments in EMPLOYEES.

Solution

1. Step 1: Understand the filter operation

   The code filters rows where DEPARTMENT equals 'SALES'.

2. Step 2: Understand the count method

   Count returns the number of rows in the filtered DataFrame.

3. Final Answer:

   The number of rows where DEPARTMENT is 'SALES'. -> Option A

4. Quick Check:

   filter + count = rows matching condition [OK]

Hint: Filter then count means count matching rows [OK]

Common Mistakes:

• Thinking count returns total rows without filter
• Confusing count with number of columns
• Assuming count returns distinct values

4. You wrote this Snowpark code but get an error:

df = session.table('EMPLOYEES')
filtered = df.filter(df.DEPARTMENT = 'SALES')
count = filtered.count()

What is the error and how to fix it?

medium

A. Add parentheses around the filter condition.

B. Use '==' for comparison instead of '=' in the filter condition.

C. Rename 'DEPARTMENT' column to lowercase.

D. Change 'filter' to 'where' method.

Solution

1. Step 1: Identify the filter condition syntax

   The filter uses '=' which is assignment, not comparison.

2. Step 2: Correct the comparison operator

   Use '==' to compare column values in Snowpark expressions.

3. Final Answer:

   Use '==' for comparison instead of '=' in the filter condition. -> Option B

4. Quick Check:

   Comparison needs '==' not '=' [OK]

Hint: Use '==' for equality checks in filters [OK]

Common Mistakes:

• Using single '=' instead of '==' in conditions
• Thinking 'where' is required instead of 'filter'
• Assuming column names must be lowercase

5. You want to process a large dataset using Snowpark and Python. Why is it better to write your transformation code in Snowpark rather than exporting data to your local machine?

hard

A. Snowpark runs code inside Snowflake, reducing data transfer and improving security.

B. Exporting data is faster and more secure than running code inside Snowflake.

C. Snowpark only supports SQL, so Python code must run locally.

D. Local processing avoids Snowflake compute costs.

Solution

1. Step 1: Understand Snowpark's architecture

   Snowpark runs code inside Snowflake close to the data storage.

2. Step 2: Benefits of running code inside Snowflake

   This reduces data transfer time and keeps data secure by not moving it outside.

3. Final Answer:

   Snowpark runs code inside Snowflake, reducing data transfer and improving security. -> Option A

4. Quick Check:

   Code near data = less transfer + better security [OK]

Hint: Run code inside Snowflake to keep data secure and fast [OK]

Common Mistakes:

• Thinking exporting data is faster or safer
• Believing Snowpark only supports SQL
• Ignoring Snowflake compute costs benefits